def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      0
 UpperCamelCase			  =      len(UpperCamelCase_       ) - 1

 while left <= right:
  # avoid divided by 0 during interpolation
  if sorted_collection[left] == sorted_collection[right]:
   if sorted_collection[left] == item:
    return left
   else:
    return None

  UpperCamelCase			  =      left + ((item - sorted_collection[left]) * (right - left)) // (
      sorted_collection[right] - sorted_collection[left]
  )

  # out of range check
  if point < 0 or point >= len(UpperCamelCase_       ):
   return None

  UpperCamelCase			  =      sorted_collection[point]
  if current_item == item:
   return point
  else:
   if point < left:
    UpperCamelCase			  =      left
    UpperCamelCase			  =      point
   elif point > right:
    UpperCamelCase			  =      right
    UpperCamelCase			  =      point
   else:
    if item < current_item:
     UpperCamelCase			  =      point - 1
    else:
     UpperCamelCase			  =      point + 1
 return None





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Dict:







 '''simple docstring'''

 # avoid divided by 0 during interpolation
 if sorted_collection[left] == sorted_collection[right]:
  if sorted_collection[left] == item:
   return left
  else:
   return None

 UpperCamelCase			  =      left + ((item - sorted_collection[left]) * (right - left)) // (
     sorted_collection[right] - sorted_collection[left]
 )

 # out of range check
 if point < 0 or point >= len(UpperCamelCase_       ):
  return None

 if sorted_collection[point] == item:
  return point
 elif point < left:
  return interpolation_search_by_recursion(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 elif point > right:
  return interpolation_search_by_recursion(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 else:
  if sorted_collection[point] > item:
   return interpolation_search_by_recursion(
       UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					point - 1       )
  else:
   return interpolation_search_by_recursion(
       UpperCamelCase_							,					UpperCamelCase_							,					point + 1							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 if collection != sorted(UpperCamelCase_       ):
  raise ValueError("""Collection must be ascending sorted"""       )
 return True


if __name__ == "__main__":
     import sys
     _SCREAMING_SNAKE_CASE 		=      0
     if debug == 1:
          _SCREAMING_SNAKE_CASE 		=      [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
          _SCREAMING_SNAKE_CASE 		=      6_7

     _SCREAMING_SNAKE_CASE 		=      interpolation_search(collection, target)
     if result is not None:
          print(F'''{target} found at positions: {result}''')
     else:
          print("""Not found""")

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
